predict.sl.time: Prediction from an Super Learner (SL) for Censored Outcomes

In RISCA: Causal Inference and Prediction in Cohort-Based Analyses

Prediction from an Super Learner (SL) for Censored Outcomes

Description

Predict the survival of new observations based on an SL by unsing the sl.time function.

Usage

## S3 method for class 'sl.time'
predict(object, ..., newdata, newtimes)

Arguments

object	An object returned by the function sl.time.
...	Further arguments passed.
newdata	An optional data frame containing covariate values at which to produce predicted values. There must be a column for every covariate included in cov.quanti and cov.quali included in the training sample. The default value isNULL, the predicted values are computed for the subjects of the training sample.
newtimes	The times at which to produce predicted values. The default value isNULL, the predicted values are computed for the observed times in the training data frame.

Value

times	A vector of numeric values with the times of the predictions.
predictions	A matrix with the predictions of survivals of each subject (lines) for each observed times (columns).

Author(s)

Yohann Foucher <[e-mail]>

Camille Sabathe <[e-mail]>

Examples

data(dataDIVAT2)

# The training of the super learner from the first 150 individuals of the data base
sl1<-sl.time(method=c("cox.ridge", "aft.ggamma"),  metric="ribs",
  data=dataDIVAT2[1:150,],  times="times", failures="failures", pro.time = 12,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), cv=3)

# Individual prediction for 2 new subjects
pred <- predict(sl1,
  newdata=data.frame(age=c(52,52), hla=c(0,1), retransplant=c(1,1), ecd=c(0,1)))

plot(y=pred$predictions$sl[1,], x=pred$times, xlab="Time (years)", ylab="Predicted survival",
     col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

lines(y=pred$predictions$sl[2,], x=pred$times, col=2, type="l", lty=1, lwd=2)

legend("bottomright", col=c(1,2), lty=1, lwd=2, c("Subject #1", "Subject #2"))

RISCA documentation built on March 31, 2023, 11:06 p.m.